Correcting noisy labels by GAT. We transform an image to a superpixel-based graph representation and leverage GAT [1]} to correct noisy labels. Following [1]}, we perform self-attention on the nodes to compute attention coefficients. The importance of node j's features to node i is given by:
\({{e}_{ij}}=\varphi {{({{v}_{i}})}^{T}}\phi ({{v}_{j}})\) 
